Irembo

Overview:

In Rwanda’s ongoing journey toward digital transformation, e-government platforms have emerged as a cornerstone of public service delivery. Portals like Irembo have revolutionized how citizens interact with government—enabling online applications for vital services such as ID renewals, birth certificates, driving licenses, and land titles.

However, as usage increases, so too does the pressure on government agencies to provide timely, scalable, and user-friendly support services. Despite the benefits of digital systems, many Rwandans—especially those in underserved areas—continue to face long wait times, unresolved tickets, and language barriers when issues arise.

To address this growing challenge, Épique AI initiated a research partnership focused on integrating AI-powered support and escalation systems into national platforms like Irembo. The goal was to reimagine the entire government support experience—making it faster, smarter, and more inclusive through the application of machine learning.

Problem Statement:

The existing support model for many African digital government platforms relies on large human teams operating in centralized call centers or back-office environments. Tickets are often manually classified and routed, which leads to delays, miscommunication, and inefficiencies.

Additionally, the language of engagement is frequently limited to English or French—excluding a large portion of the population who are more comfortable communicating in Kinyarwanda or other local languages.

For a system like Irembo, which handles millions of transactions annually, scaling personalized support through human agents alone is unsustainable.

Citizens frequently encounter repetitive issues—such as incorrect document uploads, payment delays, or unclear application requirements—yet every instance is treated as a new problem, requiring redundant explanations and staff time. This lack of institutional memory drains resources and frustrates users.

What Rwanda needs is not just more agents, but smarter systems that can understand, learn from, and respond to citizens in real-time—while continuously improving over time.

Our Approach:

Épique AI’s approach to this challenge was multi-layered, combining machine learning, conversational AI, and localized natural language models to build a self-learning, multilingual support framework for e-government services.

We began by analyzing thousands of anonymized support queries from public service platforms. Using supervised and unsupervised machine learning, we trained models to automatically classify support issues by category, urgency, and agency responsibility.

This laid the foundation for automated ticket routing and prioritization—allowing for faster escalation and better resource allocation.

Next, we developed a recommendation engine that draws on historical resolution data to propose likely solutions based on the issue at hand. Instead of re-solving similar problems repeatedly, the system can surface relevant answers instantly, shortening the resolution cycle dramatically.

At the user-facing layer, we built prototypes of multilingual chatbots and voice assistants capable of interacting in Kinyarwanda, English, and French. To enable this, we integrated existing open-source NLP frameworks with custom-trained Kinyarwanda language models developed through a local corpus of conversational data.

This ensures that users can communicate naturally and accessibly—regardless of their language or literacy level.

We also focused on contextual learning. As users interact with the AI assistant, the system learns patterns over time, becoming more accurate in its responses and more adaptive to user behavior and phrasing.

This creates a support model that improves with every interaction, rather than degrading under scale.

Field Testing:

We conducted simulations using sample ticket data and usage patterns from Rwanda’s public platforms. The AI system was benchmarked against existing support workflows in terms of classification speed, resolution accuracy, and escalation efficiency.

Initial results showed that the AI engine could reduce average handling time by over 60%, with near-instant classification of complaints and suggestion of actionable solutions in both structured and free-text queries.

The multilingual interface proved especially valuable in reaching users from non-urban areas, where Kinyarwanda remains the dominant spoken language and digital literacy is limited.

The system was also designed to integrate with existing government IT infrastructure, using APIs to connect with databases, ticketing platforms, and citizen records—ensuring that adoption could be incremental and non-disruptive.

Key Outcomes:

Through this project, Épique AI delivered not only a functional prototype but also a blueprint for transforming how public service platforms in Africa engage with their users. Key results include:

• A self-learning support engine capable of understanding, classifying, and resolving common user issues.

• A working multilingual chatbot interface with voice-enabled interaction in Kinyarwanda.

• Demonstrated potential to reduce support backlogs, improve service accessibility, and lower operational costs.

• A replicable model that can be scaled to other sectors of government, such as education, healthcare, and taxation.

Strategic Significance:

This case study positions Épique AI at the intersection of public innovation and responsible AI deployment.

While most conversational AI development has been concentrated in the private sector, we believe the greatest impact of these technologies will be in improving citizen-state relationships—especially in countries like Rwanda where digital transformation is a national priority.

By making government support smarter, faster, and more inclusive, we’re not only increasing efficiency—we’re rebuilding trust. Citizens feel heard when their concerns are addressed quickly and in their own language. Government agencies benefit from a lighter support burden, more consistent data, and better visibility into citizen needs.

For Rwanda, this project lays the groundwork for a next-generation digital government—one where public services are responsive, adaptive, and deeply connected to the people they serve.

Conclusion:

The success of platforms like Irembo should not be measured only by how many services they digitize, but by how effectively they respond to the real-world needs of users.

By embedding AI into the heart of public support systems, Épique AI has shown that it is possible to dramatically enhance the quality and responsiveness of digital government in Rwanda.

The challenge of scaling support in a multilingual, resource-constrained environment is not insurmountable—it is a design opportunity.

Through AI-driven classification, multilingual NLP, and real-time learning, we are proving that African governments can leapfrog legacy systems and lead the world in citizen-centric innovation.